Overview
Row
Overview
SNVs/InDelsTotal variants
45SNVs
45InDels
0TIER 1 variants
0TIER 2 variants
0Row
Allelic support plot
* Tumor sequencing depth and allelic fraction of variants not provided - omitting plot.
Documentation
The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.
- TIER 1: Variants of strong clinical significance - constitutes variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are
- Found within the same tumor type/class as specified by the user, AND
- Of strong clinical evidence (i.e. part of guidelines, validated or discovered in late clinical trials (CIViC evidence levels A/B))
- overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
- TIER 2: Variants of potential clinical significance - constitutes other variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are either
- Of strong clinical evidence in other tumor types/classes than the one specified by the user, OR
- Of weak clinical evidence (early trials, case reports etc. (CIViC evidence levels C/D/E))) in the same tumor type/class as specified by the user
- overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
- TIER 3: Variants of uncertain clinical significance - includes other coding variants found in oncogenes or tumor suppressor genes
- Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
- Five or more publications in the biomedical literature that suggests an oncogenic/tumor suppressor role for a given gene (as collected from the CancerMine text-mining resource), OR
- At least two publications from CancerMine that suggests an oncogenic/tumor suppressor role for a given gene AND an existing record for the same gene as a tumor suppressor/oncogene in the Network of Cancer Genes (NCG)
- Status as oncogene is ignored if a given gene has three times as much (literature evidence) support for a role as a tumor suppressor gene (and vice versa)
- Oncogenes/tumor suppressor candidates from CancerMine/NCG that are found in the curated list of false positive cancer drivers compiled by Bailey et al. (Cell, 2018) have been excluded
- Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
- TIER 4 - includes other protein-coding variants
- NONCODING - includes other noncoding variants
Row
Global variant datatable - filters
The global variant datatable (right) can be filtered according to various criteria:
- Filtering on sequencing depth/variant allelic fraction depends on input provided by user
- Filtering performed here will only apply to the datatable and not any other visualizations presented in this page
Global variant datatable
Tier 1
Row
SNVs and InDels
TIER 1Biomarker genes
0Biomarker variants
0Diagnostic evidence items
0Prognostic evidence items
0Predictive evidence items
0Row
Tier 1 variant evidence items - filters
- No variants of strong clinical significance found.
Tier 1 - variant evidence items
- No variants of strong clinical significance found.
Tier 2
Row
SNVs and InDels
TIER 2Biomarker genes
0Biomarker variants
0Diagnostic evidence items
0Prognostic evidence items
0Predictive evidence items
0Row
Tier 2 variant evidence items - filters
- No variants of potential clinical significance found.
Tier 2 - variant evidence items
- No variants of potential clinical significance found.
Tier 3
Row
SNVs and InDels
TIER 3Total variants
0Variants in proto-oncogenes
0Variants in tumor suppressor genes
0Variants in genes with dual roles
0Row
Tier 3 - variant filters
- No other variants found in proto-oncogenes or tumor suppressor genes.
Tier 3 - variant datatable
- No other variants found in proto-oncogenes or tumor suppressor genes.
Tier 4
Row
SNVs and InDels
TIER 4Total variants
6SNVs
6InDels
0Block substitutions
0Row
Tier 4 - variant filters
Variants in TIER 4 (right panel) can be interactively explored according to various criteria :
Tier 4 - variant datatable
Noncoding
Row
SNVs and InDels
NoncodingTotal variants
39SNVs
39InDels
0Block substitutions
0Row
Noncoding - variant filters
Noncoding variants (right panel) can be interactively explored according to various criteria :
Noncoding - variant datatable
Complete biomarker set
Row
SNVs and InDels
BiomarkersBiomarker genes
0Biomarker variants
0Diagnostic evidence items
0Prognostic evidence items
0Predictive evidence items
0Row
All biomarker evidence items - filters
- No biomarkers associated with variants in the tumor sample.
All biomarker evidence items
- No biomarkers associated with variants in the tumor sample .
TMB and MSI
Row
Tumor mutational burden & MSI classification
TMB/MSI statusCoding target size
34 MbCoding mutations
6TMB estimate
0.38 mut/MbMSI prediction
MSSRow
TCGA TMB distribution
MSI evidence I
The plot below illustrates the mutational burden of indels in PCB-2020-006 (black dashed line) along with the distribution in TCGA samples for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):
Row
MSI evidence II
The plot below illustrates the fraction of indels among all calls in PCB-2020-006(black dashed line) along with the distribution in TCGA for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):
Somatic mutations in MMR genes
No variants found.
Mutational signatures
Row
Mutational Signatures (SBS)
SIGNATURESSNVs eligible for analysis
45Most dominant aetiology
NDAccuracy of signature fitting (%)
NDHigh confident kataegis events
0Row
Mutational signatures - aetiology contributions
- NOT DETERMINED (ND): The number of SNV calls (n = 45) is too limited. A minimum of 200 SNVs is currently required for a mutational signature analysis to be performed.
Mutational signatures - aetiologies
- NOT DETERMINED (ND): The number of SNV calls (n = 45) is too limited. A minimum of 200 SNVs is currently required for a mutational signature analysis to be performed.
Row
Mutational context frequency
- NOT DETERMINED (ND): The number of SNV calls (n = 45) is too limited. A minimum of SNVs is currently required for a mutational signature analysis to be performed.
Genomic distribution - rainfall
Kataegis events
Clinical trials
Row
Molecularly targeted trials
Clinical trials (Beta)Not yet recruiting
103Recruiting
559Enrolling by invitation
1Active, not recruiting
118Unknown status
0Row
Molecularly targeted trials - filters
- Ongoing or planned clinical trials in the relevant tumor type have been retrieved from clinicaltrials.gov, focusing on the subset with molecularly targeted therapies
- Key information entities (interventions/drugs, conditions) in trial records have been mapped to established thesauri (ChEMBL, NCI Thesaurus, UMLS/MedGen)
- Results from a text-mining procedure on unstructured trial text (e.g. inclusion/exclusion criteria) attempts to highlight the presence of established molecular biomarkers in cancer and relevant therapeutic contexts.
Molecularly targeted trials
Settings & Docs
Row
Settings - sample and report
The report is generated with PCGR version 0.9.0rc.
Key report configuration settings:
- Genome assembly: grch37
- Minimum sequencing depth tumor (DP, SNV/InDel): 0
- Minimum allelic fraction tumor (VAF, SNV/InDel): 0
- Minimum sequencing depth control (DP, SNV/InDel): 0
- Maximum allelic fraction control (VAF, SNV/InDel): 1
- MSI prediction: ON
- Mutational burden estimation: ON
- Mutational signatures estimation: ON
- Clinical trial inclusion: ON
Sample information:
- Tumor primary site: CNS/Brain
Documentation - sequencing assay
- Assay sequencing type (VCF): WGS
- Assay coding target size (VCF): 34 Mb
- Assay sequencing mode (VCF): Tumor-Control
Estimated properties on DNA cellularity and ploidy:
- Tumor purity: 0.9
- Tumor ploidy: 2.0
Documentaton - report data sources
The report generated with PCGR is based on the following underlying tools and knowledge resources:
- Software
- VEP - Variant Effect Predictor (v101)
- LOFTEE - Loss-Of-Function Transcript Effect Estimator (VEP plugin) (v1.0.3)
- vcfanno - Rapid annotation of VCF with other VCFs/BEDs/tabixed files (v0.3.2)
- MutationalPatterns - Comprehensive genome-wide analysis of mutational processes (v2.0.0)
- vcf2maf - VCF to MAF conversion (v1.6.17)
- vcf-validator - Validation suite for Variant Call Format (VCF) files, implemented using C++11 (v0.9.3)
- maftools - Summary, analysis and visualization of MAF files (v2.4.12)
- vt - A tool set for short variant discovery in genetic sequence data (v0.57721)
- Databases/datasets
- GENCODE - high quality reference gene annotation and experimental validation (release 35/19)
- dbNSFP - Database of non-synonymous functional predictions (20200616 (v4.1))
- Pfam - Collection of protein families/domains (2020_05 (v33.1))
- TCGA - The Cancer Genome Atlas - somatic mutations (20200722 (v25))
- ICGC-PCAWG - ICGC-Pancancer Analysis of Whole Genomes - somatic mutations (2020_01)
- TCGA-PCDM - Putative Cancer Driver Mutations based on multiple discovery approaches (2019)
- UniProtKB - Comprehensive resource of protein sequence and functional information (2020_04)
- CORUM - The comprehensive resource of mammalian protein complexes (20180903 (release 3.0))
- gnomAD - Germline variant frequencies exome-wide (r2.1 (October 2018))
- COSMIC - Catalogue of somatic mutations in cancer (90)
- dbSNP - Database of short genetic variants (153)
- 1000Genomes - Germline variant frequencies genome-wide (20130502 (phase 3))
- DisGenet - Database of gene-disease associations (2020_05 (v7.0))
- DoCM - Database of curated mutations (release 3.2)
- CancerHotspots - A resource for statistically significant mutations in cancer (2017)
- ClinVar - Database of genomic variants of clinical significance (20200803)
- CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (20200908 (release 28))
- OncoTree - Open-source ontology developed at MSK-CC for standardization of cancer type diagnosis (2020_04_01)
- DiseaseOntology - Standardized ontology for human disease (20200915)
- EFO - Experimental Factor Ontology (v3.22)
- OncoScore - Literature-based ranking of gene-cancer association (20191227)
- OpenTargetPlatform - Comprehensive and robust data integration for access to potential drug targets associated with disease (2020_06)
- ChEMBL - Manually curated database of bioactive molecules (20200518 (v27))
- KEGG - Knowledge base on the molecular interaction, reaction and relation networks (20200910)
- CIViC - Clinical interpretations of variants in cancer (20200920)
- CGI - Cancer Genome Interpreter Cancer Biomarkers Database (20180117)
Row
Documentation - report content
SNVs/InDels
The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.
- TIER 1: Variants of strong clinical significance - constitutes variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are
- Found within the same tumor type/class as specified by the user, AND
- Of strong clinical evidence (i.e. part of guidelines, validated or discovered in late clinical trials (CIViC evidence levels A/B))
- TIER 2: Variants of potential clinical significance - constitutes other variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are either
- Of strong clinical evidence in other tumor types/classes than the one specified by the user, OR
- Of weak clinical evidence (early trials, case reports etc. (CIViC evidence levels C/D/E))) in the same tumor type/class as specified by the user
- TIER 3: Variants of uncertain clinical significance - includes other coding variants found in oncogenes or tumor suppressor genes
- Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
- Five or more publications in the biomedical literature that suggests an oncogenic/tumor suppressor role for a given gene (as collected from the CancerMine text-mining resource), OR
- At least two publications from CancerMine that suggests an oncogenic/tumor suppressor role for a given gene AND an existing record for the same gene as a tumor suppressor/oncogene in the Network of Cancer Genes (NCG)
- Status as oncogene is ignored if a given gene has three times as much (literature evidence) support for a role as a tumor suppressor gene (and vice versa)
- Oncogenes/tumor suppressor candidates from CancerMine/NCG that are found in the curated list of false positive cancer drivers compiled by Bailey et al. (Cell, 2018) have been excluded
- Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
- TIER 4 - includes other protein-coding variants
- Protein-coding refers here to variants with the following consequences:
- missense_variant
- stop_gain/stop_loss
- frameshift_variant
- inframe_insertions/inframe_deletions
- splice_donor_variant/splice_acceptor_variant
- start_loss
- NONCODING - includes other non-(protein)coding variants
A complete list of reported biomarkers that associate with variants in the tumor sample (not necessarily qualifying for assignment to TIER 1/TIER 2) is also shown in a separate section.
Somatic copy number aberrations
Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:
- TIER 1: Aberrations of strong clinical significance - constitutes amplified/lost genes linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are
- Found within the same tumor type/class as specified by the user, AND
- Of strong clinical evidence (i.e. part of guidelines, validated or discovered in late clinical trials (CIViC evidence levels A/B))
- overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
- TIER 2: Aberrations of potential clinical significance - constitutes amplified/lost genes linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are either
- Of strong clinical evidence in other tumor types/classes than the one specified by the user, OR
- Of weak clinical evidence (early trials, case reports etc. (CIViC evidence levels C/D/E))) in the same tumor type/class as specified by the user
- overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications
Mutational signatures
The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)
The MutationalPatterns package (Blokzijl et al., 2018) is used to estimate the relative contribution of known mutational signatures in a single tumor sample. MutationalPatterns makes an optimal reconstruction of the mutations observed in a given sample with a reference collection of n = 67 mutational signatures. By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC tumor samples).
NOTE: This sample contains too few variants for estimation of mutational signature contributions
Tumor mutational burden (TMB)
Tumor mutational load or mutational burden is a measure of the number of mutations within a tumor genome, defined as the total number of mutations per coding area of a tumour genome. TMB may serve as a proxy for determining the number of neoantigens per tumor, which in turn has implications for response to immunotherapy. For estimation of TMB, PCGR employs the same approach as was outlined in a recently published large-scale study of TMB (Chalmers et al., 2017), i.e. counting all somatic base substitutions and indels in the protein-coding regions of the sequencing assay, including synonymous alterations.
MSI classification
Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 98.9% and a negative predictive value of 98.8% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.
Note that the MSI classifier is applied only for WGS/WES tumor-control sequencing assays.
Kataegis
Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package, applied in the study by Yin et al. (2020).
Explanation of key columns in the resulting table of potential kataegis events:
- weight.C>X: proportion of C>X mutations
- confidence: confidence degree of potential kataegis events (range: 0 to 3)
- 0 - a hypermutation with weight.C>X < 0.8;
- 1 - one hypermutation with weight.C>X >= 0.8 in a chromosome;
- 2 - two hypermutations with weight.C>X >= 0.8 in a chromosome;
- 3 - high confidence with three or more hypermutations with weight.C>X >= 0.8 in a chromosome)
References
Alexandrov, Ludmil B, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin Wei Tian Ng, Yang Wu, Arnoud Boot, et al. 2020. “The Repertoire of Mutational Signatures in Human Cancer.” Nature 578 (7793): 94–101. http://dx.doi.org/10.1038/s41586-020-1943-3.
Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Samuel A J R Aparicio, Sam Behjati, Andrew V Biankin, Graham R Bignell, et al. 2013. “Signatures of Mutational Processes in Human Cancer.” Nature 500 (7463): 415–21. https://doi.org/10.1038/nature12477.
Bailey, Matthew H, Collin Tokheim, Eduard Porta-Pardo, Sohini Sengupta, Denis Bertrand, Amila Weerasinghe, Antonio Colaprico, et al. 2018. “Comprehensive Characterization of Cancer Driver Genes and Mutations.” Cell 173 (2): 371–385.e18. http://dx.doi.org/10.1016/j.cell.2018.02.060.
Blokzijl, Francis, Roel Janssen, Ruben van Boxtel, and Edwin Cuppen. 2018. “MutationalPatterns: Comprehensive Genome-Wide Analysis of Mutational Processes.” Genome Med. 10 (1): 33. http://dx.doi.org/10.1186/s13073-018-0539-0.
Chalmers, Zachary R, Caitlin F Connelly, David Fabrizio, Laurie Gay, Siraj M Ali, Riley Ennis, Alexa Schrock, et al. 2017. “Analysis of 100,000 Human Cancer Genomes Reveals the Landscape of Tumor Mutational Burden.” Genome Med. 9 (1): 34. https://doi.org/10.1186/s13073-017-0424-2.
Cortes-Ciriano, Isidro, Sejoon Lee, Woong-Yang Park, Tae-Min Kim, and Peter J Park. 2017. “A Molecular Portrait of Microsatellite Instability Across Multiple Cancers.” Nat. Commun. 8: 15180. https://doi.org/10.1038/ncomms15180.
Griffith, Malachi, Nicholas C Spies, Kilannin Krysiak, Joshua F McMichael, Adam C Coffman, Arpad M Danos, Benjamin J Ainscough, et al. 2017. “CIViC Is a Community Knowledgebase for Expert Crowdsourcing the Clinical Interpretation of Variants in Cancer.” Nat. Genet. 49 (2): 170–74. http://dx.doi.org/10.1038/ng.3774.
Lever, Jake, Eric Y Zhao, Jasleen Grewal, Martin R Jones, and Steven J M Jones. 2019. “CancerMine: A Literature-Mined Resource for Drivers, Oncogenes and Tumor Suppressors in Cancer.” Nat. Methods 16 (6): 505–7. http://dx.doi.org/10.1038/s41592-019-0422-y.
Li, Marilyn M, Michael Datto, Eric J Duncavage, Shashikant Kulkarni, Neal I Lindeman, Somak Roy, Apostolia M Tsimberidou, et al. 2017. “Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists.” J. Mol. Diagn. 19 (1): 4–23. https://dx.doi.org/10.1016%2Fj.jmoldx.2016.10.002.
Ma, Jennifer, Jeremy Setton, Nancy Y Lee, Nadeem Riaz, and Simon N Powell. 2018. “The Therapeutic Significance of Mutational Signatures from DNA Repair Deficiency in Cancer.” Nat. Commun. 9 (1): 3292. http://dx.doi.org/10.1038/s41467-018-05228-y.
Póti, Ádám, Hella Gyergyák, Eszter Németh, Orsolya Rusz, Szilárd Tóth, Csenger Kovácsházi, Dan Chen, et al. 2019. “Correlation of Homologous Recombination Deficiency Induced Mutational Signatures with Sensitivity to PARP Inhibitors and Cytotoxic Agents.” Genome Biol. 20 (1): 240. http://dx.doi.org/10.1186/s13059-019-1867-0.
Repana, Dimitra, Joel Nulsen, Lisa Dressler, Michele Bortolomeazzi, Santhilata Kuppili Venkata, Aikaterini Tourna, Anna Yakovleva, Tommaso Palmieri, and Francesca D Ciccarelli. 2019. “The Network of Cancer Genes (NCG): A Comprehensive Catalogue of Known and Candidate Cancer Genes from Cancer Sequencing Screens.” Genome Biol. 20 (1): 1. http://dx.doi.org/10.1186/s13059-018-1612-0.
Tamborero, David, Carlota Rubio-Perez, Jordi Deu-Pons, Michael P Schroeder, Ana Vivancos, Ana Rovira, Ignasi Tusquets, et al. 2018. “Cancer Genome Interpreter Annotates the Biological and Clinical Relevance of Tumor Alterations.” Genome Med. 10 (1): 25. http://dx.doi.org/10.1186/s13073-018-0531-8.
Yin, Xia, Rui Bi, Pengfei Ma, Shengzhe Zhang, Yang Zhang, Yunheng Sun, Yi Zhang, et al. 2020. “Multiregion Whole-Genome Sequencing Depicts Intratumour Heterogeneity and Punctuated Evolution in Ovarian Clear Cell Carcinoma.” J. Med. Genet. 57 (9): 605–9. http://dx.doi.org/10.1136/jmedgenet-2019-106418.